[Qgis-developer] Kriging interpolation functionality in QGIS?

[Victor Olaya]

Kriging is not that hard to find in QGIS, you just have to go to the
Processing search box and type "Kriging" to find all the the kriging
algorithms ;-)

>From the point of view of user-friendliness, the Processing
implementation (wrapping the corresponding SAGA modules), might not be
as easy to use as some people would like, but for tools such as
kriging I am strongly against wizard-like UI's and similar elements.
ArcGIS's Statistical Analyst is great and has a wizard with a
fantastic "next" button that allows you to interpolate using all sort
of esoteric methods and will make you believe that you are creating
sound raster layers...when the truth is that, without knowledge, you
are creating rubish. I don't like to give users that wrong sensation.

People want a "Kriging for dummies" functionality, and that's is a bad
idea. Kriging is complex and it's not easy to understand the
underlying principles...but you have to understand them if you want to
use the tool. I think there's no other functionality as misused in GIS
software as this one...everyone want to use kriging because it's cool
and they have heard it's better...but without studying what it really
is.

(I get questions about this topic often, so here's my take on it and
on why the Processing implementation is like that, in case it might
help).

Regards





2015-09-28 9:46 GMT+02:00 Sjur Kolberg <Sjur.A.Kolberg at sintef.no>:
> I'd prefer Kriging to IDW also for simple jobs.
>
> Kriging is (at least) four operations:
> 0: Know the assumptions and check your data, transform if necessary and possible.
> 1: Estimate your empirical semivariogram (describing how difference increases with distance).
> 2: Select a parametric semivariogram model, and adjust parameters to fit the point cloud from 1)
> 3: Use the parametric model to calculate a weight matrix between each target location (grid point) and each observation point.
>
> Loosely speaking, the three first operations define the core of Kriging. The last operation answers the same question as IDW, both providing the coefficients (weights) in a linear combination of the input data.
>
> With Gaussian data, spatial stationarity, well-spread data points and a properly selected/tuned semivariogram model, Kriging can be shown to be 'Best linear unbiased estimator' (BLUE), and provides mathematically correct estimates of the variance at each target location. IDW promises no such thing, hence appears less dependent on assumptions. This does not imply that IDW is more robust or almost-as-good for simple tasks.
>
> For the numbers that both methods do provide, it is possible to evaluate and compare the two using leave-one-out cross validation.
>
> With just a reasonable a priori judgment for the range parameter, my guess is that Kriging will beat IDW in cross-validation for most problems, including notoriously assumption-breaking daily precipitation data. With some scripting, it is also possible to optimise Kriging's semivariogram parameters by minimising the cross-validation RMSE. This does not qualify for being BLUE or for trusting the variance estimates, though.
>
> All this said, the extra burden of finding Kriging in one of QGIS' plugins, may serve as a warning that there is more to it than just plug-and play.
>
> Sjur  :-)
>
>
>
>> -----Original Message-----
>> From: qgis-developer-bounces at lists.osgeo.org [mailto:qgis-developer-
>> bounces at lists.osgeo.org] On Behalf Of Barry Rowlingson
>> Sent: 26. september 2015 19:43
>> To: Stefan Keller
>> Cc: qgis-developer at lists.osgeo.org
>> Subject: Re: [Qgis-developer] Kriging interpolation functionality in QGIS?
>>
>> On Fri, Sep 25, 2015 at 6:46 PM, Stefan Keller <sfkeller at gmail.com> wrote:
>> > Hi Barry
>> >
>> > Many thanks for your explanations and hints.
>> > So from a pragmatic point-of-view ("80/20 pareto rule"):
>> > Do you think Inverse Distance Weighting (IDW) would do the job as
>> > well, since Kriging has so many parameters to fiddle around and to
>> understand?
>>
>>  Depends on what "the job" is. To get an impression of the overall trend of a set
>> of samples - IDW is probably fine. But to get a
>> *principled* (ie based on a statistical model) set of estimates over a grid *with
>> honest estimates of uncertainty* so you can answer probabilistic questions (like
>> "what's the chance that the soil over there will contain 1ug gold/tonne?") you
>> need something like kriging.
>>
>>  Barry
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